An intelligent monitoring method for a drop-out fuse

Abstract

The present application relates to the field of drop-out fuse monitoring, and relates to a kind of drop-out fuse intelligent monitoring method, the present application includes the following steps: through the sensing unit of fuse body, three kinds of multi-modal parameters of electrical, mechanical, environment are collected, including load current, contact temperature, fuse tube inclination, vibration waveform and environmental temperature and humidity;Adopt event-triggered sampling switching mechanism, low-frequency sampling is executed under normal steady state, when detecting any trigger condition of current mutation, temperature rise anomaly or vibration impact, automatically switch to high-frequency full-parameter acquisition;Extract four kinds of core features to construct feature vector, output five kinds of state discrimination results such as normal operation, fuse deterioration early warning through pre-trained hierarchical discrimination model;Base onrush current energy and contact resistance increment, combined with environmental temperature correction, evaluate fuse deterioration grade;According to environmental temperature and historical load, dynamically adjust alarm threshold, when determining fault or early warning, report alarm event and corresponding high-frequency recording data to cloud master station.

Description

Technical Field

[0001] This invention relates to the field of drop-out fuse monitoring, and specifically to an intelligent monitoring method for drop-out fuses. Background Technology

[0002] Drop-out fuses are widely used protection devices on 10kV distribution line branches and the primary side of pole-mounted distribution transformers. They rely on the fuse wire melting under overload and the fuse tube falling back down to form a visible break, thus providing both short-circuit / overload protection and power isolation. Due to their simple structure, convenient installation, and low cost, they are deployed extensively in urban and rural distribution networks and are an important component of the distribution network protection system.

[0003] For a long time, the operational status of drop-out fuses has relied primarily on manual inspections. However, distribution network equipment is scattered across a wide area, with many outdoor devices located in remote regions such as fields and mountains. Inspection cycles are generally long, resulting in high labor costs. When faults such as fuse blowouts or fuse tube drops occur, they are often not detected and located immediately, leading to longer power outages and impacting power supply reliability.

[0004] In recent years, some online fuse monitoring solutions have emerged in the industry, but they still have many limitations in practical applications. Some solutions only collect a single parameter such as load current or contact temperature, which can only roughly determine the on / off state. They cannot identify early deterioration of the fuse wire, nor can they distinguish the specific causes of fuse tube drop—short circuit blowing, external impact detachment, and normal power outages by maintenance personnel will all manifest as changes in fuse tube tilt angle, easily generating false alarms and increasing the unnecessary workload of maintenance personnel. Some solutions use a continuous high-frequency sampling mode to capture transient fault waveforms, resulting in high equipment power consumption. Outdoor pole-mounted equipment mostly relies on induction power or battery power, and high power consumption will significantly shorten the equipment's battery life, making it difficult to operate stably for a long time. In addition, most monitoring solutions use fixed alarm thresholds, without considering the dynamic changes in ambient temperature and actual load levels. In summer high temperatures or when the line is under light load for a long time, missed alarms or false alarms are likely to occur, resulting in insufficient alarm adaptability.

[0005] Based on the above situation, there is an urgent need for an intelligent monitoring method that can identify the status of fuses from multiple dimensions, balance low power consumption and high accuracy, and dynamically assess the degradation trend, so as to improve the operation and maintenance efficiency and operational reliability of drop-out fuses. Summary of the Invention

[0006] To address the problems of existing technologies, this invention provides an intelligent monitoring method for drop-out fuses.

[0007] The objective of this invention can be achieved through the following technical solution: A smart monitoring method for drop-out fuses includes the following steps: S1. Synchronous acquisition of multi-modal state parameters: Through the sensing unit deployed on the body of the drop-out fuse, electrical parameters, mechanical state parameters and environmental parameters are acquired synchronously. The electrical parameters include the instantaneous value of the load current and the contact temperature. The mechanical state parameters include the three-dimensional tilt angle of the fuse tube and the vibration and impact waveform. The environmental parameters include the ambient temperature and ambient humidity. S2, Event-triggered sampling mode switching: Real-time judgment of the current operating status. When in normal steady state, low-frequency sampling mode is executed. When any trigger condition such as sudden current change, abnormal temperature rise or vibration shock is detected, it automatically switches to high-frequency sampling mode and starts full parameter synchronous acquisition. S3. Multi-feature fusion state classification and discrimination: Extract four core features: current mutation rate, arc duration, vibration impact amplitude and fuse inclination angle change, construct feature vector input to pre-trained classification and discrimination model, and output the current state category of fuse. The state category includes at least five categories: normal operation, fuse deterioration warning, short circuit fuse drop, external force accidental drop, and manual tripping operation. S4. Dynamic assessment of deterioration trend: Based on historical operating data, the cumulative current energy and contact resistance increment are calculated, and combined with the ambient temperature correction factor, the remaining life assessment value and deterioration level of the fuse are generated. S5, Adaptive Threshold Alarm and Data Upload: The alarm judgment threshold is dynamically adjusted based on the real-time ambient temperature and historical load baseline. When a fault or warning state is determined, the alarm event and the high-frequency waveform data of the corresponding time period are actively reported to the cloud master station.

[0008] In a further improvement, in step S1, the multimodal state parameters are synchronously acquired using a unified time scale, specifically as follows: Using the current sampling clock as the reference time stamp, the contact temperature, fuse tilt angle, and vibration signal are stamped with corresponding timestamps to ensure that the time synchronization error of each parameter data is no greater than 1ms; the sampling rate of vibration and shock waveform is no less than 10kHz, and the current sampling rate is no less than 1kHz in high-frequency mode.

[0009] In a further improvement, the event triggering conditions in step S2 specifically include: Current surge triggering: The rate of change of current within adjacent sampling periods exceeds 30% of the rated current; Temperature rise anomaly trigger: The difference between the contact temperature and the ambient temperature rises by more than 8°C within 5 minutes; Vibration and shock triggering: The triaxial accelerometer detected an impact signal with an amplitude exceeding 5g; When any trigger condition is met, the system switches from low-frequency sampling mode to high-frequency sampling mode and continues sampling for at least 30 seconds before automatically switching back to low-frequency mode.

[0010] In a further improvement, the discrimination logic of the hierarchical discrimination model in step S3 is as follows: When the fuse tube tilt angle changes from the working angle to the drop angle, accompanied by a sudden drop in current to zero and the presence of short-term arcing and impact vibration characteristics, it is determined to be a short-circuit fuse drop. When the inclination angle of the molten tube changes to the drop angle, but there is no arc light characteristic, no sudden change in current, and the impact vibration amplitude exceeds the set threshold, it is determined to be an accidental drop caused by external force. When the fuse tube tilt angle changes to a drop angle, accompanied by operational impact vibration characteristics and the current decreases in an orderly step manner, it is determined to be a manual circuit breaker operation.

[0011] In a further improvement, the arc light characteristic is identified by detecting the intensity and duration of the discharge ultraviolet pulse using an ultraviolet photosensitive sensor. When the intensity of the ultraviolet pulse exceeds 8dB and the duration is in the range of 10ms to 500ms, it is determined that there is a melting arc light.

[0012] In a further improvement, step S4, the assessment of the fuse degradation trend, specifically includes: S41. Calculate the cumulative current energy: Accumulate the current energy since the fuse was put into operation according to the formula, where I is the effective value of the load current and is the sampling interval; S42. Calculate the contact resistance increment: Based on the difference between the contact temperature rise and the reference temperature rise under the same load current, calculate the relative increment of the contact resistance. S43. Generate degradation level: Combine the ratio of cumulative current energy to rated allowable current energy with the contact resistance increment according to weight, and output four levels: normal, mild degradation, moderate degradation, and severe degradation.

[0013] In a further improvement, in step S42, the contact resistance increment is calculated using the temperature difference ratio method: Under the same load current, the ratio of the real-time contact temperature rise to the initial reference temperature rise is used as a characterization value of the relative increment of contact resistance; where the contact temperature rise is the contact temperature minus the ambient temperature at the same time.

[0014] In a further improvement, the adaptive threshold adjustment in step S5 specifically involves: A preset baseline alarm threshold is set, and a temperature correction coefficient is introduced based on the real-time ambient temperature. For every 10°C increase in ambient temperature, the temperature alarm threshold is reduced by 5%. At the same time, the current warning baseline is dynamically updated based on the load peak of the past 7 days. When the load is continuously lower than 30% of the rated value, the overload warning threshold is lowered accordingly.

[0015] In a further improvement, in step S5, the data upload adopts an event-driven and timed heartbeat mechanism: Under normal conditions, a status heartbeat data packet is uploaded every 30 minutes, including average current, maximum temperature and equipment status identifier; when a fault or warning is triggered, an alarm event packet is immediately uploaded, along with high-frequency waveform data for 10 seconds before and after the trigger.

[0016] Compared with existing technologies, the beneficial effects of the intelligent monitoring method for drop-out fuses of this invention are as follows: By integrating three types of multimodal parameters—electrical, mechanical, and environmental—and extracting four core features—current, arc, vibration, and tilt angle—for hierarchical discrimination, it can accurately distinguish five states: normal operation, fuse deterioration warning, short circuit fuse failure, external force accidental failure, and manual disconnection. This solves the problem that traditional single-parameter monitoring cannot distinguish the cause of failure, and significantly reduces invalid alarms and on-site investigation workload.

[0017] It adopts an event-triggered sampling level switching mechanism. Under normal steady state, it performs low-frequency sampling to control power consumption. When abnormal signals such as sudden current change, abnormal temperature rise, vibration and shock are detected, it automatically switches to high-frequency sampling to fully record the transient process. While ensuring the integrity of fault data, it significantly reduces the average operating power consumption of the equipment, is more suitable for the power supply conditions of outdoor pole-mounted equipment, and can effectively extend the equipment's battery life.

[0018] The degree of fuse degradation is quantified from two dimensions: cumulative current energy and contact resistance increment. Combined with ambient temperature correction, the degradation level and remaining life assessment value are output. The monitoring extends from post-fault response to pre-fault early warning, assists maintenance personnel in arranging maintenance plans as needed, and promotes the transformation of distribution network maintenance from periodic maintenance to condition-based maintenance.

[0019] The alarm judgment threshold is dynamically adjusted based on real-time ambient temperature and historical load baseline. The temperature alarm threshold is lowered accordingly in high-temperature environments, and the current warning baseline is lowered synchronously in light-load conditions. This effectively avoids the problem of missed alarms and false alarms under fixed thresholds in different seasons and load levels, and the alarm judgment is more in line with the actual operating conditions. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0021] The following describes the embodiments and appendices. Figure 1 The technical solution of the present invention will be further described below.

[0022] Example 1 A smart monitoring method for drop-out fuses includes the following steps: S1. Synchronous acquisition of multi-modal state parameters: Through the sensing unit deployed on the body of the drop-out fuse, electrical parameters, mechanical state parameters and environmental parameters are acquired synchronously. The electrical parameters include the instantaneous value of the load current and the contact temperature. The mechanical state parameters include the three-dimensional tilt angle of the fuse tube and the vibration and impact waveform. The environmental parameters include the ambient temperature and ambient humidity. S2, Event-triggered sampling mode switching: Real-time judgment of the current operating status. When in normal steady state, low-frequency sampling mode is executed. When any trigger condition such as sudden current change, abnormal temperature rise or vibration shock is detected, it automatically switches to high-frequency sampling mode and starts full parameter synchronous acquisition. S3. Multi-feature fusion state classification and discrimination: Extract four core features: current mutation rate, arc duration, vibration impact amplitude and fuse inclination angle change, construct feature vector input to pre-trained classification and discrimination model, and output the current state category of fuse. The state category includes at least five categories: normal operation, fuse deterioration warning, short circuit fuse drop, external force accidental drop, and manual tripping operation. S4. Dynamic assessment of deterioration trend: Based on historical operating data, the cumulative current energy and contact resistance increment are calculated, and combined with the ambient temperature correction factor, the remaining life assessment value and deterioration level of the fuse are generated. S5, Adaptive Threshold Alarm and Data Upload: The alarm judgment threshold is dynamically adjusted based on the real-time ambient temperature and historical load baseline. When a fault or warning state is determined, the alarm event and the high-frequency waveform data of the corresponding time period are actively reported to the cloud master station.

[0023] like Figure 1 As shown, the working principle of this invention is as follows: From a theoretical perspective, the S1 multimodal state parameter synchronous acquisition covers information from three dimensions: electrical, mechanical, and environmental, breaking the information limitations of a single parameter. Electrical parameters directly reflect the circuit's load level and on / off state, mechanical state parameters capture the fuse's attitude changes and impact actions, and environmental parameters are used to correct measurement deviations caused by environmental interference. These three types of data corroborate each other, providing multidimensional data support for subsequent state judgment.

[0024] The S2 event-triggered sampling range switching is designed to balance device power consumption with fault recording accuracy. Under normal steady-state conditions, the fuse state changes slowly, requiring no high-density sampling; low-frequency mode is sufficient for operational monitoring. When signals such as sudden current changes, abnormal temperature rises, or vibrations occur that may accompany state changes, the system automatically switches to high-frequency mode to fully record the transient process, thus controlling average power consumption while ensuring the integrity of fault data.

[0025] The S3 multi-feature fusion state classification system is designed to address the pain point of "difficulty in distinguishing the causes of fuse tube drops". By extracting four core features with the highest discriminative power—current, arcing, vibration, and tilt angle—and using a pre-trained model for fusion decision-making, it no longer relies on a single tilt angle or current threshold to determine faults. It can subdivide five operating states, achieving "not only knowing that a drop has occurred, but also knowing why it occurred".

[0026] S4 dynamic assessment of degradation trends extends the monitoring perspective from "post-fault judgment" to "pre-degradation early warning". The aging of fuses is a slow process caused by the combined effects of heat accumulation and contact degradation. By quantifying two core indicators, cumulative current energy and contact resistance increment, and combining them with ambient temperature correction, the remaining life and degradation level of fuses can be assessed relatively objectively, supporting condition-based maintenance.

[0027] S5 adaptive threshold alarm and data upload, on the one hand, dynamically adjusts alarm thresholds to adapt to different environments and load conditions, reducing false alarms and missed alarms caused by fixed thresholds; on the other hand, through differentiated data upload strategies, it ensures timely reporting of fault information while reducing the communication and data processing pressure during normal operation.

[0028] This method can accurately identify the operating status of fuses and dynamically assess their degradation trends with low operating power consumption, effectively shortening fault location time and reducing the workload of manual inspections. At the same time, it can accurately distinguish between fault drops and normal operation, reducing the proportion of invalid alarms. It plays a positive role in improving the intelligent level of distribution network operation and maintenance and ensuring power supply reliability.

[0029] As a further preferred embodiment, in step S1, the multimodal state parameters are synchronously acquired using a unified time scale, specifically as follows: Using the current sampling clock as the reference time stamp, the contact temperature, fuse tilt angle, and vibration signal are stamped with corresponding timestamps to ensure that the time synchronization error of each parameter data is no greater than 1ms; the sampling rate of vibration and shock waveform is no less than 10kHz, and the current sampling rate is no less than 1kHz in high-frequency mode.

[0030] The sampling clocks of different types of sensors are independent of each other. If the time deviation of the data of each parameter is too large, it will cause transient features such as current surges, vibration shocks, and tilt angle changes to be misaligned on the time axis. This will easily lead to feature matching errors during subsequent multi-feature fusion and discrimination, affecting the accuracy of the judgment. This solution uses the highly stable current sampling clock as the global reference time stamp, and matches the synchronization timestamps of the sampling points of other channels such as temperature, tilt angle, and vibration one by one, controlling the cross-channel time synchronization error within 1ms, and ensuring that the performance of transient events on each parameter can correspond to the same time node. At the same time, for transient signals such as vibration shocks that are short in duration and change rapidly, a high sampling rate of no less than 10kHz is set to ensure that the waveform details of the drop and collision process can be fully captured; the high-frequency sampling rate of current is no less than 1kHz, which can accurately calculate the current surge rate and identify the transient change process.

[0031] The unified time stamp effectively ensures the temporal consistency of multimodal data, providing a reliable data foundation for subsequent multi-feature joint discrimination; the differentiated sampling rate settings not only meet the sampling accuracy requirements of transient signals, but also avoid unnecessary power consumption caused by uniform high-frequency sampling across all channels, achieving a good balance between data accuracy and equipment power consumption.

[0032] As a further preferred embodiment, in step S2, the event triggering conditions specifically include: Current surge triggering: The rate of change of current within adjacent sampling periods exceeds 30% of the rated current; Temperature rise anomaly trigger: The difference between the contact temperature and the ambient temperature rises by more than 8°C within 5 minutes; Vibration and shock triggering: The triaxial accelerometer detected an impact signal with an amplitude exceeding 5g; When any trigger condition is met, the system switches from low-frequency sampling mode to high-frequency sampling mode and continues sampling for at least 30 seconds before automatically switching back to low-frequency mode.

[0033] The core of sampling level switching lies in the rationality of the trigger threshold. If the threshold is set too loosely, normal load fluctuations and minor vibrations may trigger high-frequency mode, unnecessarily increasing equipment power consumption. If the threshold is set too strictly, it may miss real fault transient processes, negating the significance of high-frequency sampling. This solution sets three complementary trigger conditions, corresponding to different types of abnormal scenarios: current surge triggering targets electrical condition surges such as short-circuit faults and tripping operations, using a 30% change rate of rated current as the threshold to filter interference from daily load fluctuations; temperature rise anomaly triggering targets thermal faults such as poor contact and slow overload. These anomalies do not produce current surges but are accompanied by continuous temperature rises, using an 8°C temperature rise within 5 minutes as the criterion to identify non-sudden-type abnormal conditions; vibration and shock triggering targets mechanical actions such as fuse drops and external impacts, using an acceleration amplitude of 5g as the threshold to effectively distinguish between minor vibrations and impact events during normal equipment operation. Setting a high-frequency hold duration of at least 30 seconds ensures complete coverage of the entire fault process from occurrence to termination, preventing a switch back to low-frequency mode before the transient process is fully recorded.

[0034] The three types of triggering conditions cover three abnormal scenarios: electrical, thermal, and mechanical. They complement each other, ensuring that faults are not missed due to the failure of a single threshold, nor are sampling modes frequently switched due to excessive sensitivity. The 30-second high-frequency holding time can completely record the waveform of the entire fault process, providing sufficient data support for subsequent fault cause analysis and status determination.

[0035] As a further preferred embodiment, in step S3, the discrimination logic of the hierarchical discrimination model is specifically as follows: When the fuse tube tilt angle changes from the working angle to the drop angle, accompanied by a sudden drop in current to zero and the presence of short-term arcing and impact vibration characteristics, it is determined to be a short-circuit fuse drop. When the inclination angle of the molten tube changes to the drop angle, but there is no arc light characteristic, no sudden change in current, and the impact vibration amplitude exceeds the set threshold, it is determined to be an accidental drop caused by external force. When the fuse tube tilt angle changes to a drop angle, accompanied by operational impact vibration characteristics and the current decreases in an orderly step manner, it is determined to be a manual circuit breaker operation.

[0036] Changes in fuse tube tilt angle are a common phenomenon across various operating conditions. Tilt angle alone cannot distinguish between faults and normal operation. Furthermore, different causes of fuse drops will exhibit different combinations of characteristics in electrical, arcing, and vibration dimensions. The essence of a short-circuit fuse drop is the fuse wire breaking due to heat, generating an arc during the process. The current drops instantly to zero, and the fuse tube falls under its own weight, generating impact vibration. Therefore, it simultaneously possesses four characteristics: tilt angle change, sudden current drop, arcing, and impact vibration. Accidental drops due to external forces, such as collisions or strong winds, cause the fuse tube to detach unexpectedly. The circuit itself does not experience a short circuit fault, so there is no arcing or sudden current change; only large-amplitude impact vibration and tilt angle change are observed. Manual disconnection involves maintenance personnel using an insulated rod to orderly disconnect the circuit. The current decreases stepwise during the disconnection process, and the vibration waveform generated by this operation differs significantly from the impact waveforms of short-circuit fuse breaks and external force collisions, exhibiting typical operational impact characteristics. A combined discrimination logic designed based on these characteristic differences can accurately distinguish between different types of fuse drops.

[0037] By using a multi-feature combination discrimination logic, it can effectively distinguish three different causes of fuse drop states, greatly reducing the situation of misjudging external force accidental drops and normal operation and maintenance operations as short circuit faults, and reducing the workload of maintenance personnel in ineffective on-site troubleshooting. The discrimination logic is closely related to the actual on-site working conditions, has strong interpretability, and is also easy to further optimize and adjust based on on-site operation data.

[0038] As a further preferred embodiment, the arc light characteristics are identified by detecting the intensity and duration of the discharge ultraviolet pulse using an ultraviolet photosensitive sensor. When the intensity of the ultraviolet pulse exceeds 8dB and the duration is in the range of 10ms to 500ms, it is determined that there is a melting arc light.

[0039] During the melting process of a fuse wire, an electric arc is generated. This arc discharge radiates high-intensity ultraviolet light, while the ultraviolet components in natural sunlight and ambient lighting are relatively weak. Therefore, detecting ultraviolet pulses can effectively identify arc light, and its resistance to visible light interference is superior to ordinary visible light detection. Setting an intensity threshold of 8dB is mainly used to filter out weak ultraviolet background noise in sunlight and ambient stray light, avoiding misjudgment caused by everyday ambient light. Setting a duration range of 10ms to 500ms matches the typical duration of arcing during the melting of a drop-out fuse. Ultraviolet pulses with too short a duration are likely interferences such as electrostatic discharge or instantaneous reflection, while those with too long a duration are usually continuous discharges or other external ultraviolet light sources, rather than an arc generated by the fuse melting.

[0040] The arc detection method based on ultraviolet pulses can still work stably in outdoor sunlight environment and has strong resistance to ambient light interference. Combining intensity and duration dual threshold judgment can effectively eliminate various environmental interferences, improve the accuracy of arc recognition, and provide reliable feature basis for the identification of short circuit fuse status.

[0041] As a further preferred embodiment, step S4, the assessment of the fuse degradation trend, specifically includes: S41. Calculate the cumulative current energy: Accumulate the current energy since the fuse was put into operation according to the formula, where I is the effective value of the load current and is the sampling interval; S42. Calculate the contact resistance increment: Based on the difference between the contact temperature rise and the reference temperature rise under the same load current, calculate the relative increment of the contact resistance. S43. Generate degradation level: Combine the ratio of cumulative current energy to rated allowable current energy with the contact resistance increment according to weight, and output four levels: normal, mild degradation, moderate degradation, and severe degradation.

[0042] The degradation of fuses and contacts is a slow, cumulative process, mainly affected by two factors: First, the heat accumulation effect generated by long-term current flow. The heat generated when current passes through the fuse will continue to act on the fuse material, causing the fuse metal to slowly oxidize, grains to grow, and mechanical strength and fusing characteristics to gradually decrease. The more current energy accumulates, the higher the degree of degradation usually is. Second, the local overheating caused by increased contact resistance. Oxidation of the contact surface and decrease in contact pressure will lead to an increase in contact resistance, which will further aggravate local heating and accelerate the degradation of the contact parts.

[0043] This assessment method starts from two core dimensions: S41 calculates the cumulative current energy by integration to quantify the degree of heat accumulation on fuse life; S42 indirectly infers the change in contact resistance by the temperature rise of the contact to reflect the deterioration of the contact condition; S43 integrates the proportion of current energy and the increase in contact resistance according to weight and divides them into four deterioration levels to adapt to the graded maintenance strategy of power distribution network operation and maintenance.

[0044] Compared to a single parameter, the two-dimensional degradation assessment can more comprehensively reflect the health status of fuses. The four-level degradation classification also matches the maintenance rhythm of daily operation and maintenance. Maintenance personnel can reasonably arrange replacement and maintenance plans according to different degradation levels, ensuring the safe operation of equipment while avoiding the cost waste caused by over-maintenance. This helps to promote the transformation of distribution network from "periodic maintenance" to "condition-based maintenance".

[0045] As a further preferred embodiment, in step S42, the contact resistance increment is calculated using the temperature difference ratio method: Under the same load current, the ratio of the real-time contact temperature rise to the initial reference temperature rise is used as a characterization value of the relative increment of contact resistance; where the contact temperature rise is the contact temperature minus the ambient temperature at the same time.

[0046] The steady-state temperature rise of the contacts is mainly caused by Joule heating from the contact resistance. Under the same load current, the heating power is directly proportional to the contact resistance, and the steady-state contact temperature rise is also approximately positively correlated with the contact resistance. Since direct measurement of contact resistance requires power outage and disassembly of the equipment, which is difficult to achieve during online operation, an indirect temperature difference ratio method is used for characterization. In the calculation, the contact temperature is first subtracted from the ambient temperature to obtain the contact temperature rise, eliminating the influence of ambient temperature changes on the absolute contact temperature. Then, the contact temperature rise during the initial operation of the equipment, when the contact condition is good, is selected as the baseline value. Under the same load current, the real-time temperature rise is compared with the baseline temperature rise; the ratio of the two can approximately reflect the relative increase in contact resistance.

[0047] The temperature difference ratio method does not require additional measurement hardware. It can calculate the relative change in contact resistance using only existing temperature and current data, making it low-cost and easy to apply in online monitoring scenarios. At the same time, by subtracting the ambient temperature and matching the same load current, it ensures the accuracy of the evaluation results to a certain extent and can effectively identify the early deterioration trend of poor contact.

[0048] As a further preferred embodiment, in step S5, the adaptive threshold adjustment specifically involves: A preset baseline alarm threshold is set, and a temperature correction coefficient is introduced based on the real-time ambient temperature. For every 10°C increase in ambient temperature, the temperature alarm threshold is reduced by 5%. At the same time, the current warning baseline is dynamically updated based on the load peak of the past 7 days. When the load is continuously lower than 30% of the rated value, the overload warning threshold is lowered accordingly.

[0049] The main problem with fixed alarm thresholds is their inability to adapt to dynamically changing operating scenarios. For temperature alarms, the higher the ambient temperature, the higher the initial reference temperature of the contacts. Under the same heating conditions, the absolute temperature of the contacts is more likely to reach the fixed alarm threshold. However, the actual thermal margin of the equipment is actually lower at this time. Therefore, when the ambient temperature rises, it is better to reduce the temperature alarm threshold to better match the actual heat tolerance of the equipment and avoid missed alarms during high-temperature seasons.

[0050] For current alarms, if the line is under light load for a long time and the normal operating current is far below the rated value, if the overload warning threshold is still set according to the rated value, a small abnormal increase in current may not trigger the warning, resulting in the early detection of potential overload. Therefore, the warning baseline is dynamically updated based on the load peak of the past 7 days, and the threshold is lowered accordingly under light load conditions to improve the warning sensitivity in light load scenarios.

[0051] Adaptive threshold adjustment allows alarm criteria to better adapt to different seasons and load levels, reducing missed alarms during high summer temperatures and delayed warnings under light load conditions. This controls the false alarm rate while improving the timeliness and rationality of alarms.

[0052] As a further preferred embodiment, in step S5, the data upload adopts an event-driven and timed heartbeat mechanism: Under normal conditions, a status heartbeat data packet is uploaded every 30 minutes, including average current, maximum temperature and equipment status identifier; when a fault or warning is triggered, an alarm event packet is immediately uploaded, along with high-frequency waveform data for 10 seconds before and after the trigger.

[0053] As a further preferred embodiment, in step S5, the data upload adopts an event-driven and timed heartbeat mechanism: Under normal conditions, a status heartbeat data packet is uploaded every 30 minutes, including average current, maximum temperature and equipment status identifier; when a fault or warning is triggered, an alarm event packet is immediately uploaded, along with high-frequency waveform data for 10 seconds before and after the trigger.

[0054] Outdoor pole-mounted monitoring equipment mostly uses wireless communication, which has relatively limited communication traffic and power consumption. Therefore, the data upload strategy needs to balance information timeliness and operational economy. A timed heartbeat mechanism is used for online monitoring under normal conditions. A 30-minute upload interval ensures that the main station can monitor the equipment's online status and basic operating data in real time without generating excessive communication traffic and power consumption. An event-driven instant reporting mechanism ensures that important events such as faults and warnings are pushed to the main station immediately, facilitating rapid response from maintenance personnel. High-frequency waveform data recorded 10 seconds before and after each trigger comprehensively covers the pre-fault warning stage and the transient process after the fault occurs, meeting the needs of fault analysis while avoiding the waste of traffic caused by uploading excessively long data periods.

[0055] The heartbeat and event-driven upload mechanism significantly reduces the amount of data transmitted during normal operation while ensuring the real-time nature of fault information. This helps save communication traffic and reduce device power consumption, effectively extending the battery life of battery-powered outdoor devices. At the same time, the accompanying short-time high-frequency waveform data also provides sufficient data support for fault review and cause analysis on the main station side.

[0056] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for intelligent monitoring of drop-out fuses, characterized in that, Includes the following steps: S1. Synchronous acquisition of multi-modal state parameters: Through the sensing unit deployed on the body of the drop-out fuse, electrical parameters, mechanical state parameters and environmental parameters are acquired synchronously. The electrical parameters include the instantaneous value of the load current and the contact temperature. The mechanical state parameters include the three-dimensional tilt angle of the fuse tube and the vibration and impact waveform. The environmental parameters include the ambient temperature and ambient humidity. S2, Event-triggered sampling mode switching: Real-time judgment of the current operating status. When in normal steady state, low-frequency sampling mode is executed. When any trigger condition such as sudden current change, abnormal temperature rise or vibration shock is detected, it automatically switches to high-frequency sampling mode and starts full parameter synchronous acquisition. S3. Multi-feature fusion state classification and discrimination: Extract four core features: current mutation rate, arc duration, vibration impact amplitude and fuse inclination angle change, construct feature vector input to pre-trained classification and discrimination model, and output the current state category of fuse. The state category includes at least five categories: normal operation, fuse deterioration warning, short circuit fuse drop, external force accidental drop, and manual tripping operation. S4. Dynamic assessment of deterioration trend: Based on historical operating data, the cumulative current energy and contact resistance increment are calculated, and combined with the ambient temperature correction factor, the remaining life assessment value and deterioration level of the fuse are generated. S5, Adaptive Threshold Alarm and Data Upload: The alarm judgment threshold is dynamically adjusted based on the real-time ambient temperature and historical load baseline. When a fault or warning state is determined, the alarm event and the high-frequency waveform data of the corresponding time period are actively reported to the cloud master station.

2. The intelligent monitoring method for drop-out fuses according to claim 1, characterized in that, In step S1, the multimodal state parameters are synchronously acquired using a unified time scale, specifically as follows: Using the current sampling clock as the reference time stamp, the contact temperature, fuse tilt angle, and vibration signal are stamped with corresponding timestamps to ensure that the time synchronization error of each parameter data is no greater than 1ms; the sampling rate of vibration and shock waveform is no less than 10kHz, and the current sampling rate is no less than 1kHz in high-frequency mode.

3. The intelligent monitoring method for drop-out fuses according to claim 1, characterized in that, In step S2, the event triggering conditions specifically include: Current surge triggering: The rate of change of current within adjacent sampling periods exceeds 30% of the rated current; Temperature rise anomaly trigger: The difference between the contact temperature and the ambient temperature rises by more than 8°C within 5 minutes; Vibration and shock triggering: The triaxial accelerometer detected an impact signal with an amplitude exceeding 5g; When any trigger condition is met, the system switches from low-frequency sampling mode to high-frequency sampling mode and continues sampling for at least 30 seconds before automatically switching back to low-frequency mode.

4. The intelligent monitoring method for drop-out fuses according to claim 1, characterized in that, In step S3, the discrimination logic of the hierarchical discrimination model is as follows: When the fuse tube tilt angle changes from the working angle to the drop angle, accompanied by a sudden drop in current to zero and the presence of short-term arcing and impact vibration characteristics, it is determined to be a short-circuit fuse drop. When the inclination angle of the fuse tube changes to the drop angle, but there is no arc light characteristic, no sudden change in current, and the impact vibration amplitude exceeds the set threshold, it is determined to be an accidental drop caused by external force. When the fuse tube tilt angle changes to a drop angle, accompanied by operational impact vibration characteristics and the current decreases in an orderly step manner, it is determined to be a manual circuit breaker operation.

5. The intelligent monitoring method for drop-out fuses according to claim 4, characterized in that, The arc characteristics are identified by detecting the intensity and duration of the discharge ultraviolet pulse using an ultraviolet photosensitive sensor. When the intensity of the ultraviolet pulse exceeds 8dB and the duration is in the range of 10ms to 500ms, it is determined that there is a melting arc.

6. The intelligent monitoring method for drop-out fuses according to claim 1, characterized in that, In step S4, the assessment of the fuse degradation trend specifically includes: S41. Calculate the cumulative current energy: according to the formula... The cumulative current-carrying energy since the fuse was put into operation, where I is the effective value of the load current. The sampling interval; S42. Calculate the contact resistance increment: Based on the difference between the contact temperature rise and the reference temperature rise under the same load current, calculate the relative increment of the contact resistance. S43. Generate degradation level: Combine the ratio of cumulative current energy to rated allowable current energy with the contact resistance increment according to weight, and output four levels: normal, mild degradation, moderate degradation, and severe degradation.

7. The intelligent monitoring method for drop-out fuses according to claim 6, characterized in that... In step S42, the contact resistance increment is calculated using the temperature difference proportional method: Under the same load current, the ratio of the real-time contact temperature rise to the initial reference temperature rise is used as a characterization value of the relative increment of contact resistance; where the contact temperature rise is the contact temperature minus the ambient temperature at the same time.

8. The intelligent monitoring method for drop-out fuses according to claim 1, characterized in that, In step S5, the adaptive threshold adjustment specifically involves: A preset baseline alarm threshold is set, and a temperature correction coefficient is introduced based on the real-time ambient temperature. For every 10°C increase in ambient temperature, the temperature alarm threshold is reduced by 5%. At the same time, the current warning baseline is dynamically updated based on the load peak of the past 7 days. When the load is continuously lower than 30% of the rated value, the overload warning threshold is lowered accordingly.

9. The intelligent monitoring method for drop-out fuses according to claim 1, characterized in that, In step S5, data uploading adopts an event-driven and timed heartbeat mechanism: Under normal conditions, a status heartbeat data packet is uploaded every 30 minutes, including average current, maximum temperature and equipment status identifier; when a fault or warning is triggered, an alarm event packet is immediately uploaded, along with high-frequency waveform data for 10 seconds before and after the trigger.

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